2019

Conférence

IEEE Congress on Evolutionary Computation. pp 1604-161.

Feature selection and construction are important
pre-processing techniques in machine learning and data mining.
They may allow not only dimensionality reduction but also
classifier accuracy and efficiency improvement. Feature selection
aims at selecting relevant features from the original feature set,
which could be less informative to achieve good performance.
Feature construction may work well as it creates new highlevel features, but these features do not have the same degree
of importance, which makes the use of weighted-features construction a very challenging topic. In this paper, we propose a
bi-level evolutionary approach for efficient feature selection and
simultaneous feature construction and feature weighting, called
Bi-level Weighted-Features Construction (BWFC). The basic idea
of our BWFC is to exploit the bi-level model for performing
feature selection and weighted-features construction with the
aim of finding an optimal subset of features combinations. Our
approach has been assessed on six high-dimensional datasets and
compared against three existing approaches, using three different
classifiers for accuracy evaluation. Experimental results show
that our proposed algorithm gives competitive and better results
with respect to the state-of-the-art algorithms

@inproceedings{hammami2019weighted,
author = {Marwa Hammami and Slim Bechikh and Chih-Chung Hung and Lamjed Ben Said},
title = {Weighted-Feature Construction as a Bi-level Optimization Problem},
booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
year = {2019},
pages = {1604--1611}
}